# coding=utf-8
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# This code is adapted from https://github.com/huggingface/transformers
# with modifications to run transformers on mindspore.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Testing suite for the MindSpore Glm4 model."""

import inspect

import numpy as np
import pytest
import torch
import transformers

import mindspore as ms

from tests.modeling_test_utils import (
    MS_DTYPE_MAPPING,
    PT_DTYPE_MAPPING,
    compute_diffs,
    generalized_parse_args,
    get_modules,
)
from tests.transformers_tests.models.modeling_common import ids_numpy

DTYPE_AND_THRESHOLDS = {"fp32": 5e-4, "fp16": 5e-3, "bf16": 5e-2}
# graph mode is not supported in this model
MODES = [1]


if transformers.__version__ >= "4.51.3":
    from transformers import Glm4Config

    class Glm4ModelTester:
        config_class = Glm4Config

        def __init__(
            self,
            batch_size=13,
            seq_length=7,
            is_training=True,
            use_input_mask=True,
            use_token_type_ids=False,
            use_labels=True,
            vocab_size=99,
            hidden_size=32,
            num_hidden_layers=2,
            num_attention_heads=4,
            num_key_value_heads=2,
            intermediate_size=37,
            hidden_act="gelu",
            hidden_dropout_prob=0.1,
            attention_probs_dropout_prob=0.1,
            max_position_embeddings=512,
            type_vocab_size=16,
            type_sequence_label_size=2,
            initializer_range=0.02,
            num_labels=3,
            num_choices=4,
            pad_token_id=0,
            scope=None,
            attn_implementation="eager",
        ):
            self.batch_size = batch_size
            self.seq_length = seq_length
            self.is_training = is_training
            self.use_input_mask = use_input_mask
            self.use_token_type_ids = use_token_type_ids
            self.use_labels = use_labels
            self.vocab_size = vocab_size
            self.hidden_size = hidden_size
            self.num_hidden_layers = num_hidden_layers
            self.num_attention_heads = num_attention_heads
            self.num_key_value_heads = num_key_value_heads
            self.intermediate_size = intermediate_size
            self.hidden_act = hidden_act
            self.hidden_dropout_prob = hidden_dropout_prob
            self.attention_probs_dropout_prob = attention_probs_dropout_prob
            self.max_position_embeddings = max_position_embeddings
            self.type_vocab_size = type_vocab_size
            self.type_sequence_label_size = type_sequence_label_size
            self.initializer_range = initializer_range
            self.num_labels = num_labels
            self.num_choices = num_choices
            self.pad_token_id = pad_token_id
            self.scope = scope
            self.head_dim = self.hidden_size // self.num_attention_heads
            self.attn_implementation = attn_implementation

        # Copied from tests.models.mistral.test_modeling_mistral.MistralModelTester.prepare_config_and_inputs
        def prepare_config_and_inputs(self):
            input_ids = ids_numpy([self.batch_size, self.seq_length], self.vocab_size)

            input_mask = None
            if self.use_input_mask:
                input_mask = np.tril(np.ones_like(input_ids))

            token_type_ids = None
            if self.use_token_type_ids:
                token_type_ids = ids_numpy([self.batch_size, self.seq_length], self.type_vocab_size)

            sequence_labels = None
            token_labels = None
            choice_labels = None
            if self.use_labels:
                sequence_labels = ids_numpy([self.batch_size], self.type_sequence_label_size)
                token_labels = ids_numpy([self.batch_size, self.seq_length], self.num_labels)
                choice_labels = ids_numpy([self.batch_size], self.num_choices)

            config = self.get_config()

            return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

        def get_config(self):
            return self.config_class(
                vocab_size=self.vocab_size,
                hidden_size=self.hidden_size,
                num_hidden_layers=self.num_hidden_layers,
                num_attention_heads=self.num_attention_heads,
                num_key_value_heads=self.num_key_value_heads,
                intermediate_size=self.intermediate_size,
                hidden_act=self.hidden_act,
                hidden_dropout_prob=self.hidden_dropout_prob,
                attention_probs_dropout_prob=self.attention_probs_dropout_prob,
                max_position_embeddings=self.max_position_embeddings,
                type_vocab_size=self.type_vocab_size,
                is_decoder=False,
                initializer_range=self.initializer_range,
                pad_token_id=self.pad_token_id,
                head_dim=self.head_dim,
                attn_implementation=self.attn_implementation,
            )

    model_tester = Glm4ModelTester()
    (
        config,
        input_ids,
        token_type_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
    ) = model_tester.prepare_config_and_inputs()

    GLM4_CASES = [
        [
            "Glm4Model",
            "transformers.Glm4Model",
            "mindone.transformers.Glm4Model",
            (config,),
            {},
            (input_ids,),
            {
                "attention_mask": input_mask,
            },
            {
                "last_hidden_state": 0,
            },
        ],
    ]

    @pytest.mark.parametrize(
        "name,pt_module,ms_module,init_args,init_kwargs,inputs_args,inputs_kwargs,outputs_map,dtype,mode",
        [
            case
            + [
                dtype,
            ]
            + [
                mode,
            ]
            for case in GLM4_CASES
            for dtype in DTYPE_AND_THRESHOLDS.keys()
            for mode in MODES
        ],
    )
    def test_named_modules(
        name,
        pt_module,
        ms_module,
        init_args,
        init_kwargs,
        inputs_args,
        inputs_kwargs,
        outputs_map,
        dtype,
        mode,
    ):
        ms.set_context(mode=mode)

        (
            pt_model,
            ms_model,
            pt_dtype,
            ms_dtype,
        ) = get_modules(pt_module, ms_module, dtype, *init_args, **init_kwargs)
        pt_inputs_args, pt_inputs_kwargs, ms_inputs_args, ms_inputs_kwargs = generalized_parse_args(
            pt_dtype, ms_dtype, *inputs_args, **inputs_kwargs
        )

        # set `hidden_dtype` if requiring, for some modules always compute in float
        # precision and require specific `hidden_dtype` to cast before return
        if "hidden_dtype" in inspect.signature(pt_model.forward).parameters:
            pt_inputs_kwargs.update({"hidden_dtype": PT_DTYPE_MAPPING[pt_dtype]})
            ms_inputs_kwargs.update({"hidden_dtype": MS_DTYPE_MAPPING[ms_dtype]})

        with torch.no_grad():
            pt_outputs = pt_model(*pt_inputs_args, **pt_inputs_kwargs)
        ms_outputs = ms_model(*ms_inputs_args, **ms_inputs_kwargs)
        # print("ms:", ms_outputs)
        # print("pt:", pt_outputs)
        if outputs_map:
            pt_outputs_n = []
            ms_outputs_n = []
            for pt_key, ms_idx in outputs_map.items():
                # print("===map", pt_key, ms_idx)
                pt_output = getattr(pt_outputs, pt_key)
                ms_output = ms_outputs[ms_idx]
                if isinstance(pt_output, (list, tuple)):
                    pt_outputs_n += list(pt_output)
                    ms_outputs_n += list(ms_output)
                else:
                    pt_outputs_n.append(pt_output)
                    ms_outputs_n.append(ms_output)
            diffs = compute_diffs(pt_outputs_n, ms_outputs_n)
        else:
            diffs = compute_diffs(pt_outputs, ms_outputs)

        THRESHOLD = DTYPE_AND_THRESHOLDS[ms_dtype]
        assert (np.array(diffs) < THRESHOLD).all(), (
            f"ms_dtype: {ms_dtype}, pt_type:{pt_dtype}, "
            f"Outputs({np.array(diffs).tolist()}) has diff bigger than {THRESHOLD}"
        )
